{"title":"听觉康复亚症状阈值运动训练的优化算法","authors":"D. Sueaseenak, Pavarisa Sangsai, Piyamas Detyong","doi":"10.1145/3168776.3168782","DOIUrl":null,"url":null,"abstract":"This paper presents the comparison study of the speech recognition system for the Thai language in the noise of the different environment. The well-known algorithms, such as MLP, SVM, GMM, HMM, VQ, DTW, DNN and End to End were used in this research. A test was conduced with 50 men and 50 women subjects during 5-60 years old. The proposed method consists of several parts which are (i) the feature extraction by Mel-frequency cepstral coefficients (MFCC) algorithm, (ii) The learning and decision process. The performance testing of the systems by the Ling's six sounds, such as ah, mm, oo, ee, sh and ss. The experiment results of our proposed method show that the accuracy of the system more than 80 percent.","PeriodicalId":253305,"journal":{"name":"Proceedings of the 2017 4th International Conference on Biomedical and Bioinformatics Engineering","volume":"115 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Optimal Algorithm of Sub-symptom Threshold Exercise Training for Aural Habilitation/Rehabilitation\",\"authors\":\"D. Sueaseenak, Pavarisa Sangsai, Piyamas Detyong\",\"doi\":\"10.1145/3168776.3168782\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents the comparison study of the speech recognition system for the Thai language in the noise of the different environment. The well-known algorithms, such as MLP, SVM, GMM, HMM, VQ, DTW, DNN and End to End were used in this research. A test was conduced with 50 men and 50 women subjects during 5-60 years old. The proposed method consists of several parts which are (i) the feature extraction by Mel-frequency cepstral coefficients (MFCC) algorithm, (ii) The learning and decision process. The performance testing of the systems by the Ling's six sounds, such as ah, mm, oo, ee, sh and ss. The experiment results of our proposed method show that the accuracy of the system more than 80 percent.\",\"PeriodicalId\":253305,\"journal\":{\"name\":\"Proceedings of the 2017 4th International Conference on Biomedical and Bioinformatics Engineering\",\"volume\":\"115 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-11-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2017 4th International Conference on Biomedical and Bioinformatics Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3168776.3168782\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2017 4th International Conference on Biomedical and Bioinformatics Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3168776.3168782","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
本文对不同噪声环境下的泰语语音识别系统进行了对比研究。本研究采用了MLP、SVM、GMM、HMM、VQ、DTW、DNN、End to End等知名算法。在5-60岁期间,对50名男性和50名女性受试者进行了测试。该方法由几个部分组成:(1)Mel-frequency倒谱系数(MFCC)算法的特征提取;(2)学习和决策过程。通过对“ah”、“mm”、“oo”、“ee”、“sh”、“ss”等六种“陵音”进行系统性能测试,结果表明,该方法的识别准确率在80%以上。
The Optimal Algorithm of Sub-symptom Threshold Exercise Training for Aural Habilitation/Rehabilitation
This paper presents the comparison study of the speech recognition system for the Thai language in the noise of the different environment. The well-known algorithms, such as MLP, SVM, GMM, HMM, VQ, DTW, DNN and End to End were used in this research. A test was conduced with 50 men and 50 women subjects during 5-60 years old. The proposed method consists of several parts which are (i) the feature extraction by Mel-frequency cepstral coefficients (MFCC) algorithm, (ii) The learning and decision process. The performance testing of the systems by the Ling's six sounds, such as ah, mm, oo, ee, sh and ss. The experiment results of our proposed method show that the accuracy of the system more than 80 percent.